Inerter effects for running robots with mechanical impedance

This paper presents inerer effect for achieving high-speed running of legged robots. The previous simplest biped robot with mechanical impedance consisted of a mass and a telescopic leg with a spring. However, the running speed of the robot is limited by the natural period of the model, which cannot be freely designed. Our proposed method overcomes this limitation by virtue of the inerter. The effectiveness of the proposed method is demonstrated through a mathematical analysis and numerical simulations.2017 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2017), 5-8 December, 2017, Macau, Maca

Analysis of fast bipedal walking using mechanism of actively controlled wobbling mass

In this study, a novel approach was developed to achieve fast bipedal walking by using an actively controlled wobbling mass. Bipedal robots capable of achieving energy efficient limit cycle walking have been developed, and researchers have studied methods to increase their walking speed. When humans walk, their arm swinging is coordinated with the walking phases, generating a regular symmetrical motion about the torso. The bipedal robots with a wobbling mass in the torso mimicked the arm swinging by the proposed control method. We demonstrated that the proposed method is capable of increasing the bipedal walking speed

Linear model predictive control with lifted bilinear models by Koopman-based approach

This study proposes a linear Model Predictive Control (MPC) method that combines high prediction accuracy with low computational cost, using a lifted bilinear model based on Koopman theory. In recent years, there has been a growing interest in approaches to learning prediction models that determine the performance of MPC through lifting linearization and lifting bilinearization based on Koopman theory. In these methods, a linear or bilinear model reflecting nonlinear characteristics of the target system can be obtained by lifting the states to a higher dimensional space with observable functions (observables). In particular, lifting bilinearization provides more accurate models for the nonlinear input affine system, but when combined with MPC, it requires solving a nonlinear optimization problem, which is computationally expensive. Therefore, in this study, we formulate a linear MPC using a lifted bilinear model that can make accurate predictions for the input affine system, thereby realizing an MPC algorithm with high accuracy and low computational cost. In the proposed method, we first formulate a prediction error correction method for the lifted bilinear model by introducing constraints based on the observables and corrective inputs. Furthermore, we propose a linear MPC that can make accurate predictions using the lifted bilinear model by utilizing prior-predicted states based on the optimal solution at the previous time. We evaluate the effectiveness of the proposed method through numerical simulations using a planar drone

Robust Control Barrier Function for Systems Affected by a Class of Mismatched Disturbances

This paper proposes a robust exponential control barrier function (RECBF) for systems affected by a class of mismatched disturbances, which forces system states to remain in a given safety set expressed by constraint functions. We consider the case that given constraint functions have different relative degrees for control input and disturbances due to the property of mismatched disturbances, and we extend a concept of the nominal exponential control barrier function (ECBF) to such cases. As a main result, we show RECBF conditions to guarantee invariance of the given safety set and formulate a convex optimization based controller with the RECBF conditions. In particular, we combine a disturbance estimation using Gaussian process regression, which is one of the machine learning methods, with the controller to make use of good properties of RECBF conditions. This formulation enables us to realize robust disturbance compensation based on experimental data, and it can be easily applied to practical systems. We show the effectiveness of the proposed controller through a numerical simulation of a magnetic ball levitation system having model uncertainty